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Mobile edge computing (MEC) utilizes wireless access network to provide powerful computing resources for mobile users to improve the user experience, which mainly includes two aspects: time and energy consumption. Time refers to the latency consumed to process user tasks, while energy consumption refers to the total energy consumed in processing ta...
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Computation offloading has become a popular solution to support computationally intensive and latency-sensitive applications by transferring computing tasks to mobile edge servers (MESs) for execution, which is known as mobile/multi-access edge computing (MEC). To improve the MEC performance, it is required to design an optimal offloading strategy...
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Citations
... Under this paradigm, many algorithms have been designed to make the optimal offloading decisions. For instance, studies in [7], [8] examine the scenario where tasks are offloaded from a single user to a single nearby server. There have also been studies that explore tasks from multiple users, optimizing not only the offloading decisions (whether to offload a task or determining the offloading ratio) but also the allocation of resources to each user. ...
The rise of the Internet of Things and edge computing has shifted computing resources closer to end-users, benefiting numerous delay-sensitive, computation-intensive applications. To speed up computation, distributed computing is a promising technique that allows parallel execution of tasks across multiple compute nodes. However, current research predominantly revolves around the master-worker paradigm, limiting resource sharing within one-hop neighborhoods. This limitation can render distributed computing ineffective in scenarios with limited nearby resources or constrained/dynamic connectivity. In this paper, we address this limitation by introducing a new distributed computing framework that extends resource sharing beyond one-hop neighborhoods through exploring layered network structures and multi-hop routing. Our framework involves transforming the network graph into a sink tree and formulating a joint optimization problem based on the layered tree structure for task allocation and scheduling. To solve this problem, we propose two exact methods that find optimal solutions and three heuristic strategies to improve efficiency and scalability. The performances of these methods are analyzed and evaluated through theoretical analyses and comprehensive simulation studies. The results demonstrate their promising performances over the traditional distributed computing and computation offloading strategies.
... However, the implementation of MEC in the IoV domain comes with its own set of challenges. Task offloading, in particular, contends with the unpredictability of network connectivity, the stringent requirements for data privacy, and the complexity involved in harmonizing a range of computational resources [15], [16]. Blockchain technology, renowned for its data security and integrity capabilities, offers a promising solution to these challenges [17], [18]. ...
... The task processing latency λ ent at edge node en for task t is expressed as (15). ...
The Internet of Vehicles (IoV) represents a paradigm shift in vehicular communication, aiming to enhance traffic efficiency, safety, and the driving experience by leveraging interconnected vehicles. Despite its promise, the IoV faces challenges such as efficient task offloading, energy management, and data security. Mobile Edge Computing (MEC) emerges as a solution to some of these challenges by bringing computational resources closer to the vehicular network’s edge, yet it raises critical concerns regarding resource management, service continuity, and scalability in dynamic vehicular environments. Addressing both IoV and MEC challenges necessitates robust and dynamic optimization mechanisms. In response to these challenges, our study introduces a multi-objective approach using Double Deep Q-Networks (DDQN), a cutting-edge application of Deep Reinforcement Learning (DRL). This algorithm combines the strengths of Deep Neural Networks (DNNs) and Deep Learning (DL) techniques, enabling dynamic decision-making that can adapt to changing conditions. By considering multiple objectives, the DDQN algorithm allows for a sophisticated trade-off analysis, efficiently balancing between the different objectives to optimize overall system performance. Through the use of Blockchain technology, known for its secure, decentralized structure, our model enhances the integrity of data, providing a reliable and efficient solution for IoV-MEC systems. We conducted a comparative analysis of our model against the standard Deep Q-Network (DQN) and Deep Deterministic Policy Gradient (DDPG) algorithms, which are prevalent in this field. Our model demonstrated significant improvements over these traditional methods: energy consumption was reduced by 26.4%, latency decreased by 6.87%, and the cost was minimized by 7.41%.
... Network scenarios involving a single edge server and multiple edge servers are important and common scenarios in the research of offloading edge computing tasks. Some researchers have considered scenarios where mobile devices offload tasks to a single server [8][9][10][11][12][13][14][15][16]. Guo S. et al. and Zhou S. et al. used minimizing latency as the optimization objective in task offloading research [8,9] [14][15][16]. ...
... Some researchers have considered scenarios where mobile devices offload tasks to a single server [8][9][10][11][12][13][14][15][16]. Guo S. et al. and Zhou S. et al. used minimizing latency as the optimization objective in task offloading research [8,9] [14][15][16]. ...
... At the same time, we note that researchers design offloading strategies mainly through three approaches: convex optimization theory [10,12,14,16,23], artificial intelligence techniques [8,17,18,21], and related heuristic algorithms [15,20]. However, using convex optimization theory to design offloading strategies can take a long time. ...
In the field of mobile edge computing (MEC) research, many studies under common research scenarios focus on the optimization of energy consumption and processing delay. Meanwhile, some researchers only discussed the task offloading of terminals to a single server, without considering the collaborative execution of task offloading by multiple servers. Motivated by their observations, this paper conducted research in the scenario of offloading tasks from a single terminal to multiple edge servers. We formulated the multi-objective optimization problem of latency and offloading reliability, and proposed an intensive task offloading strategy based on the Sequential Waiting Model(SWM). The scheme allocates tasks according to the wireless channel environment and the computing power of the server. Besides, this approach minimized the combined cost consisting of latency and offloading failure probability. Since this kind of optimization problem has been proved to be an NP-hard problem. And we designed a Niching Preservation Genetic Algorithm (NPGA), which is based on Niching Genetic Algorithm (NGA). Besides, to obtain better system performance, we simulated the influence of different computing factors on the proposed algorithm NPGA and analyzed the convergence of the NPGA. Finally, the simulation results illustrated the proposed algorithm NPGA can effectively reduce the total latency of task completion and the probability of task offloading failure. Compared with the most advanced algorithms in the previous literature, the cost function value of the combined latency and offloading failure probability reduces 2%-16%. At the same time, we conducted simulation experiments using real base station locations, and also verified that the NPGA algorithm can achieve lower cost values under multiple edge server counts.
... Neural network needs long time for training which is one of the limitations of the algorithm. Investigation on power node, topology-based power control as an essential in energy consumption reduction is done in order to have a bases of comparison by [30]. Where a connecting edge dominating set based on semi graph model. ...
Global warming has posed a serious threat to humanity's survival, the carbon dioxide(CO2) concentrations in the atmosphere are widely acknowledged as its primary cause. As aresult, global attention to energy conservation has increased dramatically in order to limit CO2emissions. Brown energy remains a big challenge; as such much need to be done to reduce theemission of carbon to the atmosphere. The traditional understanding of using the ground as atemperature moderator against harsh weather has great potential to become a strong remedyagainst the energy inefficiency of a building Heating, Ventilation and Air conditioning (HVAC).Attempt has been made to elaborate the thermal performance characteristics and conductmore research into the benefits and downsides of this passive cooling approach from manyperspectives of long-term sustainability. Edge computing (EC), a novel computing paradigminnovation, has high potential to help with digitization. A lot of researchers have done reviewin cloud computing but few work on review of energy consumption, therefore, the purpose ofthis research is to review literature about challenges and future of energy consumption in edgecomputing, the paper present challenges in energy consumption and how other researchersapplied different algorithms, simulators, performance metrics, type of energy used, andoptimization problems to address the issue of sustainable energy.
... The major concern for the partial offloading problem in MEC is how to partition the tasks and which target servers the subtasks are offloaded to. Tang et al. [19] employed the block coordinate descent method in a partial offloading problem to optimize the mixed overhead of time and energy consumption. Cao et al. [12] studied a three-node MEC system in which the tasks were split into three parts and executed in the three nodes. ...
The continual development of mobile edge computing efficiently solves the problem that mobile devices are unable to handle computation-intensive tasks due to their computation capacity and battery restrictions. In this paper, we consider mobile awareness and dynamic battery charging in a multi-user and multi-server mobile edge computing system, where various tasks are generated successively on the user devices. Servers act as learning agents and collaborate with user devices to develop task partitioning and computation resource allocation strategies. With the purpose of decreasing task failure rate and improving system utility in the long term, which is closely related to latency, energy consumption, and server cost, optimal strategies are demanded by the system. We model the joint optimization problem as a multi-agent Markov decision process game. And a deep reinforcement learning method based on the multi-agent deep deterministic policy gradient algorithm is proposed, which employs neural networks and works in a centralized training and decentralized execution manner to optimize the strategies. Finally, simulation results demonstrate the effectiveness of our proposed algorithm in terms of reducing task failure rate and improving system utility.
... Considering the underlying network architecture as shown in Fig. 1(a) and the offloading framework shown in Fig. 1 [27] Local Optimization based on univariate search Partial Energy and latency Z. Luo et al. [4] Genetic Algorithm Full Energy and latency H. Wu et al. [28] Graph based on Weighted Consumption Graph Partial Time, energy and latency G. Li et al. [16] Graph-Based Energy-Clustering Algorithm Full Energy and latency C. Sonmez et al. [10] Fuzzy-based Full Time, latency and task failure rate Q. Tang et al. [29] Optimization problem for minimizing the users' overhead Partial Time, energy and latency N. Shan et al. [14] Game Theoretic and Hierarchy Process based on Covariance (Cov-AHP) Full Time, energy and latency M. D. Hussain et al. [26] Fuzzy-based Full Time, latency and task failure rate V. Nguyen et al. [30] Fuzzy-based Full Time, latency and task failure rate Z. Abbas et al. [31] Deep Learning-based partitioning Partial Energy and latency (a) Network Architecture the fuzzy-optimal decision-making for task offloading problem can formally be defined as follows. Let, at a user U , the number of independent tasks generated at a particular instance is represented by a task set, T = T i , i = 1, 2, . . . ...
With the technological evolution of mobile devices, 5G and 6G communication and users’ demand for new generation applications viz. face recognition, image processing, augmented reality, etc., has accelerated the new computing paradigm of Mobile Edge Computing (MEC). It operates in close proximity to users by facilitating the execution of computational-intensive tasks from devices through offloading. However, the offloading decision at the device level faces many challenges due to uncertainty in various profiling parameters in modern communication technologies. Further, with the increase in the number of profiling parameters, the fuzzy-based approaches suffer inference searching overheads. In this context, a fuzzy-based approach with an optimal inference strategy is proposed to make suitable offloading decision. The proposed approach utilizes Classification and Regression Tree (CART) mechanism at the inference engine with reduced time complexity of
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) of state-of-the-art, conventional fuzzy-based offloading approaches, and has been proved to be more efficient. The performance of the proposed approach is evaluated and compared with contemporary offloading algorithms in a python-based fog and edge simulator, YAFS. The simulation results show the reduction in average task processing time, average task completion time, energy consumption, improved server utilization, and tolerance to latency and delay sensitivity for the offloaded tasks in terms of reduced task failure rates.
... As shown in table 3, the offloading paradigm is given in UAV-MEC when MD prefers to run the task to the UAV-MEC server. Partially offloading means that the MD's tasks are partially offloaded to the UAV-MEC server and partially executed locally for a better customer experience [80]. There is also relay offloading when the UAV acts as a relay to connect MD to ground BS or ground MEC [81]. ...
The lack of resource constraints for edge servers makes it difficult to simultaneously perform many Mobile Devices' (MDs) requests. The Mobile Network Operator (MNO) must then select how to delegate MD queries to its Mobile Edge Computing (MEC) server to maximize the overall benefit of admitted requests with varying latency needs. Unmanned Aerial Vehicles (UAVs) and Artificial Intelligent (AI) can increase MNO performance because of their flexibility in deployment, high mobility of UAV, and efficiency of AI algorithms. There is a trade-off between the cost incurred by the MD and the profit received by the MNO. Intelligent computing offloading to UAV-enabled MEC, on the other hand, is a promising way to bridge the gap between MDs' limited processing resources, as well as the intelligent algorithms that are utilized for computation offloading in the UAV-MEC network and the high computing demands of upcoming applications. This study looks at some of the research on the benefits of computation offloading process in the UAV-MEC network, as well as the intelligent models that are utilized for computation offloading in the UAV-MEC network. In addition, this article examines several intelligent pricing techniques in different structures in the UAV-MEC network. Finally, this work highlights some important open research issues and future research directions of Artificial Intelligent (AI) in computation offloading and applying intelligent pricing strategies in the UAV-MEC network.
... There has been a significant amount of papers in the area of task offloading strategy in MEC [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23]. ...
... There are some works such as [15][16][17][18][19] focus on balancing time and energy consumption during task offloading. Munoz et al. [15] considered the balance of energy consumption and execution delay in the offloading strategy, and proposed a dynamic scheduling mechanism. ...
... Experimental results show that it is lower than the benchmark algorithm in terms of energy consumption and time. Tang et al. [17] balanced the time and energy consumption by the task as the mixed overhead, and then performed joint optimization. In order to solve this problem, [17] used the block coordinate descent method to process each variable step by step. ...
The task offloading of mobile edge computing (MEC) is to find proper edge or cloud resources for the execution tasks to efficiently utilize resources and meet different user’s requirements. However, it is difficult for task offloading when the number of tasks and resources providers increases and to optimize multiple objectives while satisfying users’ requirements. In this paper, a new multi-objective strategy based on the biogeography-based optimization (BBO) algorithm is proposed for MEC offloading to satisfied users’ multiple requirements (the execution time, energy consumption and cost). In this strategy, a time-energy consumption model and a cost model are constructed for task offloading firstly. Based on these models, the BBO algorithm is introduced into task offloading for MEC to solve the problem of multi-objective optimization. Compared with the traditional strategies, the offloading strategy based on BBO decreases the average task completion time by an average of 25.03%, and compared with the technique for order preference by similarity to an ideal solution (TOPSIS) strategy, the BBO offloading strategy proposed in this paper reduces energy consumption 75% and cost by 36.9%. The proposed strategy can well solve the problem of multi-objective optimization in the task offloading for MEC.
... An approximation algorithm was proposed such that the compression and transmission energy of wearable devices are minimized. Tang et al. studied partial task offloading in the MEC considering the overheads of time and energy [22]. The block coordinate descent method was proposed to solve the time and energy minimization problem. ...
With the Internet of Things (IoT) and communication technologies are snowballing, various applications (e.g., e-health and face recognition) are generated by IoT devices (IoTDs). Nevertheless, these IoTDs generally have constrained computation resources. By offloading the IoT applications to be processed by the MEC servers, mobile edge computing (MEC) is envisioned as a promising and effective solution to address this problem. Meanwhile, security is a critical issue for task offloading in MEC. While plenty of studies have focused on IoT tasks offloading, many of them ignored the security issue. Moreover, many previous works ignored the resource allocation of MEC servers. In addition, as dynamic voltage scaling (DVS) technology is flexible in the design of MEC systems, we integrate this technology with task offloading. In this paper, the problem of IoT applications offloading in an MEC system is studied, whose goal is to minimize computation overheads measured by the task processing delay and energy consumption of IoTDs. The AES cryptographic technique is adopted to make sure that the security of the data of the offloaded tasks is guaranteed. An optimization problem of security-aware task offloading is formulated and solved by proposing an efficient resource-allocation scheme. Experimental results are performed to evaluate and confirm the performance of the proposed security model.
... The offloading amount furthermore, quality vary with the heterogeneous idea of the edge network gadgets. This is of high importance to the clients' administration classification in choosing the need to offload and find edge hubs [13]. Offloading is performed from a distance and locally with the accessible gadgets that are fit for lessening the inactivity is administration spread. ...
The Mobile Edge Computing (MEC) paradigm aims to satisfy user needs by offering cloud services at the user network's edge. Block chain technology combined with the Edge Computing (EC) paradigm is dependable in providing edge services based on user needs and enhancing distributed resource management with ease. This article introduces BDO-AM, or block chain-assisted data offloading for availability maximization. The non-probabilistic (NP) hardness problem of data availability caused by growing backlogs is combated by the suggested solution. This method categorizes the many instances of data delivery and availability for edge-connected end-user services and apps. In order to avoid unneeded backlogs, the classification is preceded by utilizing Naive Bayes' classification to identify the offloading instances. Independent analyses are done on the likelihood of data transfer, delivery, and unloading.
